I was a Netflix subscriber in US, not anymore. Back home in India, I tried monthly free subscription for Netflix and HotStar and only the later stayed. What really changed? The content is almost the same and so is the quality but the key was overall “Value Proposition”. A term often misunderstood in isolation, but is more a comparative measure based on prevailing environment, competition and what is found reasonable. Here, Netflix’s value proposition failed to inspire in comparison to other options.

Netflix is world’s largest steaming giant, with over 117.5 million subscribers in 190+ countries, consuming over 140 million hours of TV shows and movies everyday. It is the strongest gainer among FAANG stocks (Facebook, Amazon, Apple, Netflix and Google) with its shares already gaining 50% this year, on top of 50% gains in 2017. Its market capitalization recently crossed $130 billion, placing it comfortably in the league of media giants like Comcast and Time Warner. But Netflix’s journey in India still remains a restricted and premium only service. It is sad given India is one of the largest data and fastest growing online media market.

Netflix 2016 Vs. 2018

Netflix launched with great fanfare in India but its presence has largely remained unnoticed. While it has been testing waters and growing steadily, the time has come for it to embrace and expand with energy. Netflix recently announced seven local series, including sacred games, featuring Bollywood star Saif Ali Khan with complete Indian production team. There are three other original Indian series, Leila, Ghoul and Crocodile in pipeline. While uptick in India specific content and local production is great news, it still lags by a far distance in comparison to Netflix’s other large international markets.

Connectivity and Online Video

Launch of Reliance Jio service in 2016 has completely overhauled India’s Online landscape. It has catapulted India to numero uno position in terms of mobile data usage with world’s largest and cheapest data network. India’s in 2018 has transformed from a relatively low broadband, limited Internet mobility, low data consumption and device penetration to fastest growing across all four parameters. India’s Internet connectivity landscape (4G) now records amongst highest consumption and lowest rates, placing it well ahead of world’s most developed nations.

Mobile internet is significantly important given India is a mobile first country with smartphones becoming primary content consumption devices for majority of users. India has over 300 million smart phone users and another 400 million feature phone users who are fast transitioning to internet connected devices. The number of internet users with video capable devices is expected to reach 500 million in 2018 and projected to cross 800 million by 2021. India offers a huge online video market, growing at 35% YoY with user spending on an average 12.3 hours per week watching online content (reported by Akamai). Clearly a market not to be ignored.

What Netflix can do differently?

Netflix has been immensely successful across the world, growing and gaining customers every quarter. It has great content, unmatched quality, superior delivery technology and superb presentation but the package has gaps when it comes to Indian market. Netflix has kept its strategy intact by targeting to premium category consumers but for India it is not about the UHD and 4K content and neither it is about the latest Hollywood movie. But it is about overall customer satisfaction – content, which customers can relate to and low subscription fees. Netflix needs some serious reworking in both content and pricing to succeed in India. It is this value proposition, which matters and will make the difference. While Netflix has not publically published Indian subscribers, it is largely expected to be fewer than a million. Below are some areas which could bring a change

Partnership for subscription – Netflix needs deeper penetration among masses something it cannot achieve alone. A custom long-term contracts in partnership with dominant device / telecom player can exponentially expand its subscription penetration. The service needs to scale even with lower margins. E.g. an yearly subscription at nominal rates with like Reliance Jio / Bharti Airtel can give access to millions of potential new subscribers.

Pricing Tiers – Netflix’s current pricing is an issue and is well recognized. While it retains it global pricing policy, differentiated plans can be created to encourage subscription. An under $2-$3 plan with limited content in SD offers a low enough barrier for customers to engage and experiment with Netflix. Given Netflix already manages subscription tiers based on resolution and connections, content catalogs access policy is a doable extension.

Partnership for Content – India is a large country with many languages and regional preferences. There is a need for local content with familiar cast which audience can recognize and relate to. Ratio of Hollywood to Indian content has to shift towards later. Although Netflix has a vast library, which charms Indian viewers, it still lags local content. While a beginning is made, it has lot of ground to cover from local and regional content to partnership with Indian media houses and studios. Getting rights to sports specially Cricket IPL, World Cup can give it instant coverage and reach.

Freemium models – While ad-supported freemium models have remained outside of Netflix’s ways of business, the model has great significance for Indian subscribers who have savored YouTube video for ages. With exorbitant amount of content available for free* , any paid subscription has to offer significant benefits to get even small market share. While there will be premium set of users who will pay and subscribe the reach of service remains restricted.

Make the Price right – Among all factors, pricing is still the key since product excellence is important but value proposition is paramount.

Competition

India has a full house when it comes to Online media players with Hotstar, Voot, Sony Liv, YouTube, Amazon Prime among the largest. HotStar, in particular has been doing well with growing content offerings and a decent pricing structure. Its’ partnership with Star and Viacom and smart marketing brings variety of local and global content (like HBO, ShowTime, ABC) and hugely popular live cricket matches, coverage of IPL (Indian Premier league), and new Bollywood movies. Hotstar comes at one-third the cost of cheapest Netflix subscription and commands over 70 million subscribers, with roughly 3-5% paid subscribers.

Conclusion

Netflix focus for paid only, premium subscription is a restrictive move and keeps the mass market out of bound. Given India’s phenomenal rise in Internet penetration, growing devices, rich demographics and low data rates, all factors, which were once adverse for global players to invest in India, have turned in India’s favor. Netflix’s entry into India seems more like to have a presence rather than to make its presence felt. It needs an India focused strategy, needs to take bold decisions and invest for a longer-term play. India has the answer to provide growth Netflix wants to grow at, it is country which has potential to add 100 million subscribers to its kitty. Netflix needs to shine brighter and bolder in India.

]]>I was a Netflix subscriber in US, not anymore. Back home in India, I tried monthly free subscription for Netflix and HotStar and only the later stayed. What really changed? The content is almost the same and so is the quality but the key was overall “Value Proposition”. A term often misunderstood in isolation, but [&#8230;]http://www.mediaentertainmentinfo.com/2018/03/16-me-research-netflix-needs-to-shine-brighter-in-india.html/feed/05764http://www.mediaentertainmentinfo.com/2018/03/16-me-research-netflix-needs-to-shine-brighter-in-india.html/Top 10 areas Artificial Intelligence is leading automation in Media Industryhttp://feedproxy.google.com/~r/MediaAndEntertainmentInformation/~3/Kc3gjQi3cX8/Artificial IntelligenceBig DataMachine LearningAutomationMedia ConvergenceNitin NarangTue, 26 Sep 2017 08:55:05 PDThttps://www.mediaentertainmentinfo.com/?p=5679

In this era of data explosion, collecting data in itself is not sufficient. It needs to be processed, sliced and diced to gain insights for running and growing the business. Unfortunately, majority of data available in the world today is unstructured and hidden making it difficult to process without significant human participation. A large part of data in media industry falls squarely in this category but it has started to CHANGE.

Take any video file, and it holds large amount of unstructured data interwoven in its fabric which requires close human engagement to understand and decode. It needs human effort for doing the most basic jobs of content management, processing, interpretation, quality checking etc. before it can be marked ready for distribution. Interestingly, AI and ML algorithms and especially deep learning, is now reaching a level in parity to human accuracy to perform large part of these tasks at scale. AI is well positioned to both automate workflow activities as well as generate tremendous insights from this hidden asset “data”. As a result, media industry is witnessing several winners in domain of natural language processing (NLP), facial recognition, anomaly detection and more where AI is bringing large scale automation with unmatched efficiencies. 2107 marks an important year when AI is starting to harvest rich dividends in broadcasting, content management, post production, advertising and many more verticals. And they say, it is only the beginning of AI journey!

Predictive Analytics and Deep Learning

Predictive analytics uses a critical assumption that future behavior is likely influenced from past trends, and in most cases it holds good for a period of time. At the foundation of these predictive models are a set of hypothesis, that bind together a number of independent variables (say for content personalization – variables like age, gender, financial status, education, content interest) to build statistical correlations. It is the collective strength and degree of these correlations, that can predict a future behavior. Read here to learn more on Predictive Analytics. More recently deep learning, which uses neural networks to bring human brain like analytical capabilities, is taking machine learning to yet higher cognitive levels. By simulating human brain like response to a situation, deep learning brings a marked shift from old school brute force decision trees to something more real.

Machine Learning focus areas in Media and Entertainment Industry

AI and ML have been in academic and R&D world for last several decades and it is only in the last few years that real industry integration has started to make way. AI brings technology to automate tasks which have been largely human intensive and offers benefits from scalability, speed of computation and repeatability. It has great potential to bring serious cost savings by automating existing tasks in content management, media operations as well as improving customer engagement and experience. For example, AI can automate a complex job of audio/video sync, saving tremendous amount of manual human effort as well as cut down on human errors. The following are top ten AI transformation areas making inroads in Media & Entertainment Industry.

1. Deep Video Analysis, Translation, Transcription and Tagging – AI took several years to perfect hand writing recognition and quickly moved to natural language understanding (NLU). It is now accelerated to go beyond natural language and metadata processing to delve into deep content analysis. Transcription is becoming near real time with machine led automation converting spoken audio into readable text. We all have seen the early arrival with Alexa, Cortana and Google voice. Neural network trained systems are replacing traditional word for word conversion by adding new dimension of contextual and intent relevance. It is expected that in next 3 years, AI will completely take over transcription and translation activity and will be resident on daily use audio devices.

Deep video analysis is another interesting area leading to manifold expansion in video insights by learning scene changes, locational references, voice, facial and object recognition. This intelligence is going a long way in enriching content taxonomy and appropriate tagging of the content, which is improving accuracy of content linkage, search and association. Here AI is significantly changing the entire content management landscape with machine driven indexing, metadata-tagging, cataloging etc., turning manual processes to highly automated workflows. Video translation to multiple languages and dialects and multi lingual subtitles is helping expand content’s addressable market to far greater audiences than ever before.

2. Voice based virtual assistants – In last 2 years, voice assistants like Alexa, Google home and voice remotes like Siri and Roku have started to fade away chunky TV remotes by perfecting basic menu navigation. Coming next is intelligence for content search and discovery with help of user follow up commands. AI using supervised learning algorithms is now powering virtual assistants to combine consumer’s knowledge graphs, geo coordinates, voice inputs and rich content metadata (cast, synopsis, quotes, locations etc.) to offer personalized recommendations. This ability of virtual assistants to understand linguistic features, emotions and user intent is making them smarter, intuitive and mature conversation system adding to better customer experience. As individual digital relationships become more profound, virtual assistants are expected to play a dominant role in offering addressable video content delivery.

3. Optimized Video Encoding and Delivery- Video streaming had a major fillip with introduction of adaptive bit rate (ABR) streaming. ABR encoding creates small chunks of original file into different bit rates to service a client based on available bandwidth (Read here to understand more on streaming). AI is going the extra mile by bringing technology to improve fixed bitrate chunking to scene based encoding. AI, by learning complexity of scenes across multiple quality metrics, can determine required level of compression and given a video for encoding, the system can determine frame level complexity and optimal compression parameters while keeping track of quality. Netflix mastered this technology a while back to generate refined encoded streams even at lower bitrates. This new encoding is radically transforming ways of providing uninterrupted video to growing population of viewers in emerging economies where low bandwidth network on mobile phones is most dominant platform for watching video. AI is also improving online media player performance by optimizing required bitrate based on viewer location, network congestion, infrastructure metrics and bandwidth details.

4. Visual Recognition – Facial and object recognition is an AI area which is heavy on visual processing. It deals with identification of individuals and objects in the video and still images as well as its relative changes with time. While this kind of visual processing comes naturally to humans, it has been an uphill task for machines to crunch large data variations to reach desired level of accuracy. More recently, AI and machine learning is increasingly able to master visual perception – facial and pattern recognition, opening rich avenues in content editing and automated content creation. Wondered how Facebook and numerous photo apps do an amazing job with photo tagging of your friends; it is all AI and ML in the making

5. Anomaly Detection – In last several years, online video has grown dis-proportionally. YouTube, Facebook and online networks have further created unbounded opportunities for both amateurs and professionals to become content creators and reach mass audiences. Today, for the amount of video and images getting generated every second, it has become humanly impossible to monitor and flag inappropriate content (piracy, violence, adult, etc.). It is again machine learning services which are proving exemplary in this space with most networks creating automated AI based detection tools at the point of upload. Google’s cloud vision API is one such service achieving great results to tag content appropriately. While fake content creation has been an increasing threat from AI, it is the same AI technology coming to the rescue in restricting the malice

6. Content Fingerprinting – Working on the principle of capturing sample content snippets to create a unique fingerprint for identification, content fingerprinting has come a long way in media industry. As content continues to grow with multi-channel distribution, there is number of application where AI based fingerprinting technology is playing an important role. Some use cases are

Finding exact and similar profile media with effective search, Shazam is a live model

Micro licensing of content with block chain for payment and tracking against usage

Identification and tracking of consumer viewing behavior, measurement of commercials

7. Video Quality Assessment – Video compression has been fundamental to video to achieve reasonable bitrates for delivery. But compression being lossy, introduces impairments and artifacts like blockiness etc. Video quality assessment has always been a critical process before content distribution and has grown manifold with multi channel distribution. Traditionally two standard methods, either standalone or in conjunction are used for quality assessment. Manual human based visual analysis by playing the content and checking for errors and a more automated reference based evaluation using metrics like VQM, PSNR, MSE, SSIM and others. While the former needs significant human effort, later has its challenges with accuracy, non-real time nature and dependence on a reference model. AI and machine learning is changing it all by mastering non reference based video quality assessment. AI using extensive feature sets and learning from error patterns is able to offer near real-time quality assessment. An area of tremendous potential to automate quality control in video workflows and bring matchless efficiency in reducing content release timelines

8. Virtual Reality and Augmented Reality – AR/VR market holds great potential but the technology has largely under performed due to challenges in cost, content maturity and ease of usage. While virtual reality (VR) specializes in creating a 360 degree immersive experience, augmented reality (AR) deals with overlay of computer graphic elements on real world elements. For a large part VR/AR apps and services are still rough and AI is bringing renewed energy by improving quality of data and decision making. AI is helping with accuracy of images, better understanding of user input and intent, content correlation, contextualization, as well as content authoring to build a more immersive experience for users

9. Post Production – A large number of creative processes are based on defined rules and techniques and hence can be mastered by machine learning algorithms. AI systems have potential to automate ground work required for various creative processes from plot identification, scene selection, scripting and more. Heard aboutMorgan ? A sci-fi, AI based movie released last September had something common with the movie theme itself. The movie trailer although finalized by human editors was suggested by AI using IBM Watson. Here, Watson was trained to learn from trailers of similar theme and select critical scenes from the movie, which were later, stitched together to create final trailer. A great example where AI can select scenes, insert visual effects and build a convincing, human edited like trailer. Below are some more areas where AI is making an entry

Structural and semantic analysis of video content to help create short form video snippets for news, video segmentation as well as special interest content for fan engagement.

Script proofing, content cleanup, scene sequencing and taking first pass at film editing. Given a script context, creating multiple scene performances with rating scores for selection

Recently IBM partnered with U.S. Open to provide sports highlights, by recognizing important match moments. AI ability for quick content identification and aggregation of related content in sports and news can completely transform business of sport and news coverage as it exists today

10.Content Production

Structural and object based analysis of content has opened new avenues where AI is helping with actual content development. Learning from minute details of how an on-screen character behaves, walks, talks and all possible moods of facial expression, AI systems can create virtual performances. It is amazing to see how a real life like performance can be created – check this clipping of a speech by US President Obama which he never gave, leaving little to the imagination. AI is still making baby steps in the world of content creation and there are many areas where it can benefit the production processes

Creating virtual human characters (digital only avatars) by learning from popular features, expressions, persona and styles of popular celebrities

Automate computer graphics work in animation movies, replacing human intensive work of character animation but with far greater efficiency

Summary

Artificial Intelligence and Machine Learning has potential to impact anything and everything which is based on a set of rules, and where a pattern can be established and learned by machines. AI and ML technology has its own unexplored territory and hurdles but is positioned for greater goals and holds promise of unparalleled capabilities. With financial services, high tech and telecom rapidly adopting AI, Media and Entertainment Industry is not far behind in automating its workflow processes.

It is the human talent of creativity, ingenuity and imagination which always had a special place in media industry, but it seems not all will remain the same as AI powered automation takes over…….

]]>In this era of data explosion, collecting data in itself is not sufficient. It needs to be processed, sliced and diced to gain insights for running and growing the business. Unfortunately, majority of data available in the world today is unstructured and hidden making it difficult to process without significant human participation. A large part [&#8230;]http://www.mediaentertainmentinfo.com/2017/09/top-10-areas-artificial-intelligence-is-leading-automation-in-media-industry.html/feed/05679http://www.mediaentertainmentinfo.com/2017/09/top-10-areas-artificial-intelligence-is-leading-automation-in-media-industry.html/Online video enters age of Big Data, Predictive Analytics and Machine Learninghttp://feedproxy.google.com/~r/MediaAndEntertainmentInformation/~3/Hz3WdL7RVhc/Big DataMachine LearningOTTpredictive analyticsNitin NarangWed, 14 Dec 2016 19:47:20 PSThttps://www.mediaentertainmentinfo.com/?p=5284

As we close the doors on 2016, we are entering into most fascinating times in the history of online video. Over the top (OTT) video, which started as a complementary service to Television has unfolded into a multi-billion dollar SVOD industry, and shows no signs of slowing down. Netflix, Hulu and Amazon have emerged as new age entertainment giants, and Virtual MVPD providers are re-wiring online to bridge any gaps from traditional TV. It is thrilling to witness new events reshaping OTT services each day. Sample this, launch of DirecTVNow; cloud DVR on Sling TV, offline viewing from Netflix and live NFL games on CBS all access, all of these have happened in a period of less than last two weeks. As digital media shifts online, broadcasters and online video distributors are innovating faster than ever. First came the content, then came technology, next was original content production and now it’s the turn of big data, predictive analytics and machine learning to redefine online video.

Big Data Collection

Lack of quality data is the biggest challenge in data analytics, and historically broadcasters had little access to it. Cable and Satellite service providers controlled consumer data, and broadcasters view was best limited to Nielsen ratings from sampled audience data. But it all started to change almost a decade back with launch of VOD streaming services like YouTube, Netflix and others. Broadcasters, fearing threat of being left behind, introduced TV Everywhere services on their part, offering limited content over the top to authenticated subscribers.

Moving content online brought benefits for both subscribers and content providers. While consumers got convenience, content providers got new revenue streams and data to consumer behavior, real insights into how subscribers interact with their content. This enabled providers to capture and store every possible consumer interaction, brightening both the quality and quantity of consumer data that can now be captured. Overtime data collection expanded, and now spans to hundreds of parameters. Viewing history, play clicks, devices used, internet speed, time and duration of viewing, browsing habits, customer support interactions, record scheduling, genre preferences, channel and content switching and much more. The once small tracking clickstream had slowly transformed into big data. It is this data, which is now bringing insights to understand the subscriber. Fast-forward to today and major broadcasters and networks (HBO GO, CBS All Access etc.) are reaching directly to subscribers with full-fledged OTT services. In summary, while early VOD and TV Everywhere services provided glimpse of consumer behavior, direct OTT relationship has opened data floodgates for a 360-degree subscriber view.

Data generated directly on video platform, termed as first party, is primary and most important data for analytics. But a lot of the data is also collected from social media platforms like Facebook and Twitter, capturing real-time audience reactions to content. In addition second and third party data collected from DMP’s provide valuable correlations and enrich the analysis from primary data.

Big Data, Predictive Analytics and Machine Learning in OTT Video

Online video services have been collecting data for several years now, making it a perfect case for big data analytics. Predictive analytics, which uses techniques in data mining, statistical modeling and machine learning, is taking big data analytics to its next logical level. Predictive analysis with help of supervised machine learning algorithms is bringing powerful prescience in predicting future trends. It can help predict what content to create, need of encoding profiles, new device adoption rates, expansion needs for edge servers, geographic growth and mobile consumption trends etc. These predictions are in turn influencing decisions at the very core of online video business. Helping in putting future plans for operations, customer support, content strategy, promotions, personalization and more.

What answers can Data provide?

Famous economist Ronald Coase once quoted, “Data may not contain the answer, but if you torture the data long enough – it will confess“.And rightly so, collecting data is not the end game; neither is reporting or building dashboards. Rather it all starts with a simple question, albeit a hard one. What are we looking for, what question that we want data to answer? And only when we have the right question, we can use predictive analytics to find the answers from available big data.

Online video players like Netflix and Amazon have built extensive data analytics strategies and are leading the way video players are using data to run their business. Netflix decision to enter entertainment production was a result of data analysis, as well as its $100 million decision to outbid top TV channels to earn rights forHouse of Cards. Media networks and broadcasters although late in the game have extensive consumer data, and it is about time for them to act on their data strategies to get real answers.

Rise of Predictive Analytics in Online Video

Predictive analytics and machine learning is playing an important role in data analytics where traditional tools have hit a roadblock. It brings capabilities to conquer massive amount of data at scale and discover implicit patterns in structured and unstructured data. Below are some of the key factors, which are fueling their growth in online video.

Access of Big data sources is the key for data analytics. Today, popular online video platforms capture billions of clickstreams of user interaction data every day making it a perfect case for predictive analysis.

Access to large infrastructure, had perennially been a critical roadblock for big data analysis. Cloud services like AWS and Azure have now made infrastructure scalable, agile and accessible. It has become quite easy to quickly setup and get running a large storage and processing server farm without upfront CAPEX or technology challenges.

Role of data analysis was traditionally restricted to statisticians. But emergence of software platforms with distributed data processing has now opened data science to software developers. Open source platforms like Hadoop, Spark, H20, TensorFlow with machine learning have bridged the gap with great elegance.

Summary

Content creation and distribution is still the core business for online video players. Predictive analytics using machine learning algorithms is helping stakeholders to forecast future trends as well as empower them to take data driven decisions. It is still early days, and we are only scratching the surface in use of knowledge from historical and real-time data to influence operational, marketing and content strategies. Great revelations await us, as true potential of big data and AI gets unlocked in the coming days.

]]>As we close the doors on 2016, we are entering into most fascinating times in the history of online video. Over the top (OTT) video, which started as a complementary service to Television has unfolded into a multi-billion dollar SVOD industry, and shows no signs of slowing down. Netflix, Hulu and Amazon have emerged as new age entertainment giants, and Virtual [&#8230;]http://www.mediaentertainmentinfo.com/2016/12/online-video-enters-age-of-big-data-predictive-analytics-and-machine-learning.html/feed/15284http://www.mediaentertainmentinfo.com/2016/12/online-video-enters-age-of-big-data-predictive-analytics-and-machine-learning.html/#6 M&E Technical : How to setup single bitrate HLS streaminghttp://feedproxy.google.com/~r/MediaAndEntertainmentInformation/~3/_0r8_JB9mGE/New MediaTechnicalVideo StreamingHTML5Online videoOTTStreamingSVODNitin NarangWed, 01 Jun 2016 19:27:41 PDThttps://www.mediaentertainmentinfo.com/?p=4337

HLS or HTTP Live Streaming provides a reliable way to deliver continuous and long-form audio and video over the Internet. It has become a default standard for online delivery of audio and video serving content on host of devices and browser environments. What makes HLS a better option than RTMP or RTSP chunked streaming is deployment with standard http severs and http as communication protocol. HLS can be used to deliver a single bitrate file or in a more advanced format, multiple bitrates. Multi bitrate configuration enables receiver or client to adapt to required bitrate based on its current network conditions for an uninterrupted playback. HLS or HTTP Live Streaming (HLS) specification is available here HLS-IETF

Key benefits with HLS Streaming

Well structured and simple protocol. Playlist is accessible in text format and easy to modify.

Use of TS (transport stream) files ensures ecosystem for testing and conformance.

TS files can carry other metadata, such as SCTE 35 cues or ID3 tags (audio metadata standard for MP3 audio)

HLS is supported natively on iOS

HLS has some disadvantages also like it is not supported natively on windows platform and TS files mux audio, video and data together. The mix limits modifications to files like adding additional audio tracks etc.

HLS File structure

HTTP live streaming works on sequence of MPEG-2 TS file segments which are listed in a manifest index file. The TS media segments encapsulates both the audio and video and are typically of 10 seconds duration, but it can be easily configured. An index file provides an ordered list of the URLs of the media segment files and is saved with an .m3u8 extension. The receiver or client downloads the index file, parses the URLs and downloads media segments in the sequence for playback. The URLs for segments can be a remote HTTP resource or local file on web server, accessed using the standard http file protocol.

Index file has details on sequence number to associate chunks from different profiles, time information, type of stream, information about the chunk duration etc. Index file also has optional metadata directives signaling whether chunks can be cached and the location of decryption keys.

Setup for HLS Streaming

Deploying HTTP Live Streaming needs few simple steps. We need source content which will be streamed. If content is not in desired bitrate, it can be easily recoded to desired bitrate

Desired bitrate files can be created by recoding the mezzanine or original file to target bitrates. In the example below, we encode the file for 500 kbps which is ideal for 48op resolution using constant rate factor (constant quality), a more desired option is using 2 pass encoding process.

Next we create HLS segments and store them behind a server. Lastly we create either an HTML page or a client app to receive and playback the media. Below are the steps to have HLS streaming service up and running in no time.

Step 1 : Source content and prepare in HLS chunked files

Get Source content and check its config

In the below example, we have a 2 min mp4 file with video as avc1 and audio as aac

fileSequence is the default prefix for the generated .ts files. It can be changed using the -B option. Similarly index file is named as program_index.m3u8 by default and can be changed using -i option, duration of each segment is 10 seconds by default and can be changed using -t sec option.

Use Bento4 toolkit to create single bitrate HLS segments

Get Bento4 toolkit from here. Bento4 is a C++ library and tools designed to read and write ISO-MP4 files.

mp42hls Geography.mp4

mp42hls converts an MP4 file to a single-bitrate HLS presentation, generating TS segments and .m3u8 playlist. Segment is the default prefix for the generated .ts files, it can be changed using the –segment-url-template option. Similarly default index file is named as stream.m3u8 and can be changed using –index-filename option, duration of each segment is 10 seconds by default and can be changed using –segment-duration sec option.

M3U file format defines two key tags: EXTM3U and EXTINF. EXTINF tag is a record marker, has a unique sequence number, specific the duration and describes the media file by the URL that follows it. In the above example the first TS segment (segment-0.ts) refers to the URL which is local to the server and 14.714 denotes the duration of first segment in seconds. The EXT-X-PLAYLIST-TYPE tag provides mutability information about the event type e.g VOD. The EXT-X-MEDIA-SEQUENCE tag indicates the sequence number of the first URL that appears in a playlist file. Other important tags are

The EXT-X-VERSION tag indicates the compatibility version of the playlist file.

The EXT-X-ENDLIST tag indicates that no more media files will be added to the playlist file.

Above we created a HLS segments for a single bitrate. A more advanced usage is creating multiple bitrate files and generate manifest with respective chunked segments.

Setup Server

HTTP Live Streaming can be served from an ordinary web server e.g. apache and requires no special configuration. It is recommended to associate the MIME types of the files being served with their file extensions

.m3u8 – application/x-mpegURL

Client side code

The easiest way to distribute HTTP Live Streaming media is to create a webpage that includes the HTML5 <video> tag, using an .M3U8 playlist file as the video source. The source can also be a relative path on the web server or refer to file on a remote/CDN server location.

Need to restrict media access to designated users, it can be easily done by using encrypted streams. We will cover next how to create HLS streaming with encryption

]]>HLS or HTTP Live Streaming provides a reliable way to deliver continuous and long-form audio and video over the Internet. It has become a default standard for online delivery of audio and video serving content on host of devices and browser environments. What makes HLS a better option than RTMP or RTSP chunked streaming is deployment with [&#8230;]http://www.mediaentertainmentinfo.com/2016/06/6-me-technical-how-to-setup-single-bitrate-hls-streaming.html/feed/04337http://www.mediaentertainmentinfo.com/2016/06/6-me-technical-how-to-setup-single-bitrate-hls-streaming.html/